Ablation study. We perform an ablation study to understand the impact of different components of PONI. There are three key components that contribute to our performance: the object potential function (\(U^o\) ), the area potential function (\(U^a\) ), and the fact that they are defined only at the frontiers (F-only). We additionally study the impact of using ground-truth image segmentation (GT). In tab:ablationstudy (rows 1-4), we compare the performance of our complete model with variants that have one or more components missing. The complete model with the 3 components achieves the best performance (row 4, tab:ablationstudy). When \(U^o\)  is removed (row 3, tab:ablationstudy), both success rate and SPL drop by a good margin, indicating the value of goal-oriented search within PONI. When \(U^a\)  is removed (row 2, tab:ablationstudy), the performance drops drastically which shows the importance of a good exploratory bias for ObjectNav, echoing findings from recent work that encourage exploration for ObjectNav via rewards [1]}, [2]} and tethered policies [3]}. In row 5, we augment our complete model with the ground-truth semantic segmentation (GT). We observe that the performance improves significantly on all cases.3D semantic annotations are erroneous near the object edges. This limits the success with even GT semantics (see [4]} for more details). Since the image segmentation impacts semantic mapping and the stopping behavior for the local policy, segmentation failures are a major source of error for PONI.
